Recent technical development focused on real-time heart rate monitoring instead of postexercise evaluation of recorded data. There are several systems on the market that allow direct and real-time monitoring of several individuals at the same time. The present study compared the systems of Polar, Acentas, Activio, and Suunto in a field test with twelve subjects regarding failure quota, operating distance, and ECG validity. Moreover, the installation and use of software and hardware were evaluated with a quality rating system. Chest belts were evaluated with a questionnaire, too. Overall the system of Acentas reached the best mark of all systems, but detailed results showed that every system has its advantages and disadvantages depending on using purpose, location, and weather. So this evaluation cannot recommend a single system but rather shows strength and weakness of all systems and additionally can be used for further system improvements.

This paper presents a distributed moving horizon estimator (DMHE) based on dual decomposition. The DMHE is equivalent to a centralized Kalman filter and allows the distributed implementation of any centralized controller. This equivalence is achieved by formulating the estimation problem as a suitable convex optimization problem. The cost function is defined on a sliding window involving a finite number of past measurements. These measurements are allocated to the estimators without requiring local observability of the complete state. The communication topology of the estimators is represented by a graph and reflected in the optimization problem by additional consensus constraints. A subgradient method is used for solving the decoupled dual optimization problem. The resulting distributed algorithm alternates between updating and transmitting the primal and dual variables. Simulation results of a closed-loop weir system are provided in order to show the main features of the proposed method.

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